The use of AI in data collection and analytics is helping to shape the sustainability sector. These tools have incredible potential to help us identify new opportunities for sustainable growth across industries.
M. Rake Lingor Anggoro is a student in the M.S. in Applied Analytics (APAN) program, whose research on how AI and machine learning are influencing agriculture in Asia was recently presented at the 2025 Institute of Electrical and Electronics Engineers (IEEE) Conference on Technologies for Sustainability (SusTech). The model Anggoro built through his research, which streamlines the aggregation of data and identifies trends in successful agricultural practices, is promising not only because of its potential to influence agriculture but also because of the far-reaching applications of this work beyond a single industry.
“What excites me most is the opportunity to keep pushing the boundaries of how we connect knowledge with action,” says Anggoro. “This research has shown that it's possible to turn a wide range of publications into structured, accessible intelligence that can actually help people make better decisions, whether in government, business, or environmental work.”
Anggoro has already enjoyed career success, leading a high-stakes data transformation initiative for Indonesia’s largest private telecommunications company, where he was named Employee of the Year in 2020. Now, he’s developing skills in the APAN program to help achieve his goal of becoming a leader with mastery of the latest tools to shape business and societal outcomes.
Congratulations on presenting your paper at the IEEE SusTech 2025 Conference. What was that experience like?
Presenting at IEEE SusTech 2025 was an incredibly rewarding experience. After months of research and refinement, it was deeply gratifying to share my work with a global audience committed to sustainable technology. The opportunity to present findings from my paper was both a personal milestone and a professional highlight.
One of the most memorable moments was an exchange with a computer science professor who expressed strong interest in my methodology that sparked new ideas for how I might extend the research, particularly around cross-sector applications and refining the bibliometric techniques we used. While this wasn’t my first time presenting at an international conference, it was certainly among the most impactful. The depth of discussion, the diversity of topics, and the caliber of attendees made SusTech a powerful platform for growth.
Your paper focuses on applying AI and machine learning to sustainable agriculture in Asia. What are the most promising ways these technologies can support food security or climate resilience efforts?
AI and machine learning are becoming indispensable tools in tackling sustainable agriculture, such as food security and climate resilience, especially in Asia, where agriculture is both vital and vulnerable. Through this research, we built a model that leverages natural language processing and generative AI to systematically analyze scientific publications and extract insights about how AI/ML is being applied to sustainable agriculture use cases. Rather than manually reviewing thousands of articles, our approach allows researchers, industry leaders, and policymakers to quickly surface best practices, emerging trends, and proven methodologies.
I’m especially excited by the model’s adaptability. While this project focused on agriculture, the same framework can be extended to other domains, like healthcare, energy, or education—as long as we collaborate with subject-matter experts to tailor what the model is trained to look for. It’s a scalable way to bridge the gap between research and real-world application.
Ultimately, this type of intelligence can play a key role in informing decisions and shaping strategies where speed, accuracy, and evidence are critical.
How do you envision your findings influencing real-world decision-making—whether for policymakers, agribusinesses, or environmental groups?
The goal of this research was never just academic. It was to bridge insights with impact. By developing a model that maps AI and machine learning applications in sustainable agriculture, we’ve created a practical tool that can support real-world decisions across multiple stakeholder groups. Policymakers benefit by having an evidence-based view of what technologies are working across regions, which will help them design policies, allocate funds, and plan regulations. Agribusinesses can learn from successful case studies to shape innovation strategies, adopt technology, and differentiate themselves in the market. Finally, environmental organizations can identify research gaps to strengthen advocacy efforts and ecosystem-level interventions.
I’m driven by the idea that technology should help us solve real-world problems faster and more inclusively. This research has opened up a path to do just that, and I’m excited to see where it leads, as well as to collaborate with other passionate researchers.
What brought you to Columbia?
Pursuing a master’s in Applied Analytics at Columbia University was a strategic step toward my long-term goal of becoming a leader in data and AI. I’ve always envisioned myself eventually earning a doctorate, not just for academic achievement, but to build the kind of deep expertise and influence that can shape both business and societal outcomes. The program is a critical part of becoming the kind of data leader I aspire to be: one who combines advanced technical skill, strategic thinking, and the ability to drive change across organizations and industries.
Columbia’s Applied Analytics program stood out not only for its academic excellence but also for its access to a remarkable network of industry leaders from organizations like IBM, Nielsen, Pfizer, Microsoft, and Mercedes-Benz. I saw an opportunity to learn directly from professionals who are shaping the field, and to build meaningful relationships with peers who are equally passionate about using data for impact.
About the Program
Columbia University’s Master of Science in Applied Analytics prepares students with the practical data and leadership skills to succeed. The program combines in-depth knowledge of data analytics with the leadership, management, and communication principles and tactics necessary to impact decision-making across industries and organizational functions.
The spring 2026 application deadline for the M.S. in Applied Analytics program is November 1. Learn more about the program here. The program is available full-time and part-time, online and on-campus.
